A Data Scientist who ships to production, not just notebooks.
Churn models, LTV prediction, attribution and executive dashboards — built to be used, not just presented once.
Get Started with Data Scientist
Free 30-min strategy call. I'll review your project and respond within 24 hours.
50+ founders consulted last month
Most data science work dies in a Jupyter notebook. I build models and pipelines that plug into your actual decisions — pricing, retention, marketing spend — and keep running after I leave.
What you get
Every engagement is built around measurable outcomes — not just deliverables.
Predictive models that matter
Churn, LTV, and demand forecasting models tied directly to a business decision.
Clean, trustworthy data
ETL pipelines and data warehousing so your numbers are consistent across every dashboard.
Executive dashboards
Looker/Metabase dashboards leadership actually opens every week.
Production-ready, not just notebooks
Models deployed as APIs or scheduled jobs — not a one-off analysis.
Models that plug into decisions
A model is only useful if someone acts on it. I start from the business decision — who to retain, what to price, where to spend — and work backwards to the model, the pipeline, and the dashboard that puts it in front of the right person every week.
What’s included
- Churn, LTV and demand forecasting models
- ETL pipelines and data warehousing
- Executive dashboards (Looker, Metabase)
- Production deployment as APIs or scheduled jobs
From kickoff to results
A clear, transparent process — no surprises.
Data audit
Map every data source, check quality, and identify the highest-value modelling opportunity.
Model prototyping
Build and validate models against a clear success metric, fast.
Production deployment
Ship the model as an API, batch job, or dashboard your team can rely on.
Monitor & retrain
Set up drift monitoring so the model stays accurate as your data changes.
01What tools do you use?
Python (pandas, scikit-learn, PyTorch where needed), SQL, and BI tools like Looker Studio or Metabase.
02Can you work with our existing data warehouse?
Yes — Snowflake, BigQuery, Postgres, or a spreadsheet-based setup; I’ll work with what you have.
03Do you need a data engineering team in place?
No — I can build the pipelines myself for small-to-mid scale, and hand off documentation for your team.
04How do you measure success?
We agree on one business metric upfront — reduced churn, higher LTV, lower CAC — and the model is judged against that, not accuracy alone.
Ready to get started?
Book a free 30-minute strategy call. No pitch, no pressure — just honest advice on where to focus.